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Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future services offered by the envisioned smart cities, thanks to their agility, flexibility, and cost-efficiency. UAVs are being widely deployed in different verticals including surveillance, search and rescue missions, delivery of items, and as an infrastructure for aerial communications in future wireless networks. UAVs can be used to survey target locations, collect raw data from the ground (i.e., video streams), generate computing task(s) and offload it to the available servers for processing. In this work, we formulate a multi-objective optimization framework for both the network resource allocation and the UAV trajectory planning problem using Mixed Integer Linear Programming (MILP) optimization model. In consideration of the different stake holders that may exist in a Cloud-Fog environment, we minimize the sum of a weighted objective function, which allows network operators to tune the weights to emphasize/de-emphasize different cost functions such as the end-to-end network power consumption (EENPC), processing power consumption (PPC), UAV’s total flight distance (UAVTFD), and UAV’s total power consumption (UAVTPC). Our optimization models and results enable the optimum offloading decisions to be made under different constraints relating to EENPC, PPC, UAVTFD and UAVTPC which we explore in detail. For example, when the UAV’s propulsion efficiency (UPE) is at its worst (10% considered), offloading via the macro base station is the best choice and a maximum power saving of 34% can be achieved. Extensive studies on the UAV’s coverage path planning (CPP) and computation offloading have been conducted, but none has tackled the issue in a practical Cloud-Fog architecture in which all the elements of the access, metro and core layers are considered when evaluating the service offloading in a distributed architecture like the Cloud-Fog.

The emergence of delay-sensitive and computationally-intensive mobile applications and services pose a significant challenge for Unmanned Aerial Vehicles (UAVs) devices due to the scarcity in their resources such as computational power and battery lifetime. Mobile cloud computing has been introduced as a promising solution to overcome these limitations through task offloading. However, high-latency and security issues are considered the main challenges of this paradigm. Subsequently, the edge-cloud computing paradigm has been introduced and widely used to help to mitigate these issues. Nevertheless, the current task offloading models permit UAVs to execute their intensive tasks at the connected edge server, which leads to excessive loads due to the large number of UAVs and thereby increases the delay. Therefore, in this paper, we propose a delay-optimal task offloading approach for multi-tier edge-cloud computing in a multi-user environment. The problem is formulated as an optimization model using Integer Linear Programming (ILP) techniques to minimize the total service time of UAVs. Simulation results demonstrate that the proposed approach not only saves the service time by 33.5% and 55% for edge and cloud execution policies respectively, but also scales well for a large number of UAVs.

In the near future, the number of Internet connected objects is expected to be between 26 - 50 billion devices. This figure is expected to grow even further due to the production of miniaturized portable devices that are lightweight, energy, and cost-efficient. In this article, the entire IoT-fog-cloud architecture is modeled, the service placement problem is formulated using Mixed Integer Linear Programming (MILP) and the total power consumption is jointly minimized for processing and networking. We evaluate the distributed processing paradigm for both the un-capacitated and capacitated design settings in order to provide solutions for the long-term and short-term basis, respectively. Furthermore, four aspects of the IoT processing placement problem are examined: 1) IoT services with non-splittable tasks, 2) IoT services with splittable tasks, 3) impact of processing overheads needed for inter-service communication and 4) deployment of special-purpose data centers (SP-DCs) as opposed to the conventional general-purpose data center (GP-DC) in the core network. The results showed that for the capacitated problem, task splitting introduces power savings of up to 86% compared to 46% with non-splittable tasks. Moreover, it is observed that the overheads due to inter-service communication greatly impacts the total number of splits. However much insignificant the overhead factor, the results showed that this is not a trivial matter and hence much attention needs to be paid to this area to make the best use of the resources that are available at the edge of the network.

Data classification effectively classifies the data based on the labeled class distribution. To classify the data using the imbalanced distribution poses a significant challenge in the class inequity problem. Various data classification methods are developed in the learning framework, but proving better classification accuracy is a significant challenge in the application domain. Therefore, an effective classification method named Adam-Cuckoo search based Deep Belief Network (Adam-CS based DBN) is proposed to perform the classification process. At first, the input data is forwarded to the pre-processing stage, and then the feature selection stage. The wrapper-based feature selection model conducts the search in space with the possible parameters. The operators specify the connectivity between the states and select the features based on their state. The classification is performed using the Deep Belief Network (DBN) classifier such that the multilayer perceptron (MLP) layer of Deep Belief Network (DBN) is trained using the proposed Adam based Cuckoo search (Adam-CS) algorithm. The breeding behavior of cuckoos is integrated with the step size parameter to enhance the accuracy of the classification process. The adaptive learning rate parameter effectively estimates the moments using a sparse gradient. The proposed Adam based Cuckoo search (Adam-CS) algorithm attained better performance using the metrics, such as accuracy, specificity, and sensitivity, with 90% training data.

The continuous increase in the use of smart devices and the need for E2E smart2smart (S2S) services in IoT systems play effective and contemporary roles in the field of communication, and a large amount of resources is required. Thus, IoTs and cloud computing must be integrated. One of the results of this integration is the increase in the number of attacks and vulnerabilities in the E2E S2S message delivery service of such an IoT-cloud system. However, none of the traditional security solutions can be sufficiently regarded as a secure and lightweight mechanism for ensuring that the security requirements for E2E S2S message transmission in the IoT-cloud system are fulfilled. This work aims to provide an efficient and secure, lightweight E2E S2S message delivery function, which includes the E2E S2S secure key and biometric parameter exchange function, a bio-shared parameter and bio-key generation function, secure lightweight E2E S2S communication negotiation and secure E2E S2S lightweight message delivery. The secure, lightweight cryptographic communication procedure is negotiated between a pair of smart devices during each E2E session to minimize the power consumption required of limited-energy devices. Such a negotiation process prevents known attacks by providing responsive mutual authentication. Lightweight message delivery by the two smart devices can satisfy the basic security requirements of E2E communication and ensure that the computational cost required for a real-time system is as low as possible.